Computer systems and computational architectures are being developed that have the ability to generate answers to natural language questions. Question and answer systems typically depend on automated processes for analyzing questions and for composing answers from a large corpus of information. Traditional Natural Language Processing (NLP) applications define a plurality of corpus sets that comprise complete corpora for natural language understanding. Limits on these types of systems are imposed because of the limits on the corpora of information.
Specializations within a scholastic area provide the benefit of deep knowledge to solve difficult problems that could not be comprehended by the generalist. However, specialized fields produce independent languages, terms, symbols, and ultimately datasets that are not known outside of a specialist's field. The data sets might be used as input into custom algorithms for analysis without the need for social algorithm application.
Natural Language Processing systems ingest may corpora that are filtered and provided by users into a central staging area. Users or data modelers often create independent data sources or have knowledge sources that are not within the perspective of all members of an entire team. Too often, many different specialists have access to knowledge bases that are not shared with generalists or with specialists in different fields.
Conventional ways of sharing data for use by members of teams who might not have specialized knowledge include manual corpus selection and deletion, team communication to track candidate corpus sets, customer meetings for corpus set selection, non-conditional corpora delta loading, ad hoc NLP scaling, parallelizing corpus processing and team repository sites such wiki's and team rooms.